Welcome to PyBrain

PyBrain is a modular Machine Learning Library for Python.
Its goal is to offer flexible, easy-to-use yet still powerful algorithms
for Machine Learning Tasks and a variety of predefined environments to
test and compare your algorithms.

PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library. In fact, we came up with the name first and later reverse-engineered this quite descriptive "Backronym".

How is PyBrain different?

While there are a few machine learning libraries out there, PyBrain aims to be a very easy-to-use modular
library that can be used by entry-level students but still offers the flexibility and algorithms for state-of-the-art
research. We are constantly working on more and faster algorithms, developing new environments and
improving usability.

What PyBrain can do

PyBrain, as its written-out name already suggests, contains algorithms for neural networks, for reinforcement
learning (and the combination of the two), for unsupervised learning, and evolution. Since most of the current
problems deal with continuous state and action spaces, function approximators (like neural networks) must be used to
cope with the large dimensionality. Our library is built around neural networks in the kernel and all of the
training methods accept a neural network as the to-be-trained instance. This makes PyBrain a powerful tool for
real-life tasks.

Using PyBrain

PyBrain is open source and free to use for everyone (it is licensed under the BSD Software Licence).
Just download it and start using the algorithms and modules in your own project or have
a look at the provided tutorials and examples.
If you use PyBrain for your research, we kindly ask you to cite us
in your publications. Use the reference below or import this bibtex reference.